Segmentation and load balancing platform system

ABSTRACT

According to some embodiments, data is received indicative of a plurality of insurance claims. It may then be automatically determined that a first insurance claim is associated with a first type of insurance and that a second insurance claim is associated with a second type of insurance. The received data associated with the first insurance claim may be analyzed in accordance with first segmentation logic to determine a first segment classification appropriate for the first insurance claim. Similarly, the received data associated with the second insurance claim may be automatically analyzed in accordance with second segmentation logic to determine a second segment classification appropriate for the second insurance claim. Indications of the first and second segment classifications may then be transmitted (e.g., to a load balancing and assignment engine that automatically selects claim handlers for insurance claims).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 15/970,470 entitled Segmentation Platform System,filed May 3, 2018, which in turn is a continuation of U.S. patentapplication Ser. No. 14/530,141 entitled System For Claim DataSegmentation, filed Oct. 31, 2014, now U.S. Pat. No. 9,984,421, which inturn claims the benefit of and priority to, under 35 U.S.C. 119(e), U.S.Provisional Patent Application No. 62/046,348, entitled “System forClaim Data Segmentation and Load Balancing” and filed Sep. 5, 2014, thecontents of all of which are hereby incorporated herein by reference intheir entireties for all purposes.

FIELD

The present invention relates to computer systems and more particularlyto computer systems that provide an automated insurance claim processingsystem.

BACKGROUND

An insurer may provide payments when claims are made in connection withan insurance policy. For example, an employee who is injured whileworking might receive payments associated with a workers' compensationinsurance policy purchased by his or her employer. Similarly, a personinvolved in an automobile accident may receive a payment in connectionwith an automobile insurance policy. The insurer may assign a claimhandler to communicate with a claimant, an employer, another insurer,and/or medical service providers to help determine the appropriateamount of payment. Note that submitted claims may involve variousamounts of work by a claim handler. For example, one type or segment ofinsurance claim might be relatively straightforward while anothersegment of claims involve complex determinations of liability and/orinjury issues.

In one approach, a received insurance claim is simply assigned to aclaim handler in a random or round robin manner. This, however, mightlead to one claim handler having a significantly more complex workloadas compared to another claim handler. Moreover, manually determining thecomplexity of an insurance claim, and/or which claim handler it shouldbe assigned, can be a time consuming and error prone task, especiallywhen there are a substantial number of claims, of many different types,to be analyzed. For example, an insurer might receive tens of thousandsof new insurance claims each year (which might represent a billiondollars of potential liability). It would therefore be desirable toprovide systems and methods to facilitate the assignment of insuranceclaims to appropriate segments and/or to claim handlers in an automated,efficient, and accurate manner.

SUMMARY

According to some embodiments, systems, methods, apparatus, computerprogram code and means may facilitate the assignment of insurance claimsto appropriate segments and/or claim handlers. In some embodiments, acommunication device may receive data indicative of a plurality ofinsurance claims submitted in connection with insurance policies. It maythen be automatically determined that a first insurance claim isassociated with a first type of insurance and that a second insuranceclaim is associated with a second type of insurance. The received dataassociated with the first insurance claim may be analyzed in accordancewith first segmentation logic to determine a first segmentclassification appropriate for the first insurance claim. Similarly, thereceived data associated with the second insurance claim may beautomatically analyzed in accordance with second segmentation logic todetermine a second segment classification appropriate for the secondinsurance claim. Indications of the first and second segmentclassifications may then be transmitted (e.g., to a load balancing andassignment engine that automatically selects claim handlers forinsurance claims).

A technical effect of some embodiments of the invention is an improvedand computerized method to facilitate the assignment of insurance claimsto appropriate segments and/or claim handlers. With these and otheradvantages and features that will become hereinafter apparent, a morecomplete understanding of the nature of the invention can be obtained byreferring to the following detailed description and to the drawingsappended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to some embodiments of thepresent invention.

FIG. 2 illustrates a method that might be performed in accordance withsome embodiments.

FIG. 3 is block diagram of a segmentation system according to someembodiments of the present invention.

FIG. 4 is an example of a method that might be performed according tosome embodiments.

FIG. 5 illustrates a screen display of a configurable insurance claimsegment ranking table in accordance with some embodiments.

FIG. 6 is an example of a method that might be performed according tosome embodiments.

FIG. 7 is block diagram of a segmentation tool or platform according tosome embodiments of the present invention.

FIG. 8 is a tabular portion of an insurance claim database according tosome embodiments.

FIG. 9 is a tabular portion of a cause of injury database according tosome embodiments.

FIG. 10 is a tabular portion of an injury database according to someembodiments.

FIG. 11 is a tabular portion of a configurable parameters databaseaccording to some embodiments.

FIG. 12 is a tabular portion of a jurisdiction database according tosome embodiments.

FIG. 13 is a tabular portion of a database of other rules according tosome embodiments.

FIG. 14 illustrates a screen display of a segmentation report inaccordance with some embodiments.

FIG. 15 is block diagram of a load balancing and assignment systemaccording to some embodiments of the present invention.

FIG. 16 is a tabular portion of a claim database according to someembodiments.

FIG. 17 is a tabular portion of a claim handler database according tosome embodiments.

FIG. 18 is an example of a method that might be performed according tosome embodiments.

FIG. 19 is a graph illustrating overall numbers of insurance claims thatare currently being processed by claim handlers via an expeditedworkflow in accordance with some embodiments.

FIG. 20 illustrates a screen display of an assignment preferences tablein accordance with some embodiments.

FIG. 21 illustrates a screen display of a dedicated claim handlermanager in accordance with some embodiments.

FIG. 22 illustrates a screen display of a catastrophe management tablein accordance with some embodiments.

FIG. 23 is a partially functional block diagram that illustrates aspectsof a computer system provided in accordance with some embodiments of theinvention.

FIG. 24 is a block diagram that provides another representation ofaspects of the system of FIG. 20.

FIG. 25 is a flow chart illustrating how a predictive model might betrained according to some embodiments.

FIG. 26 illustrates predictive model inputs according to someembodiments.

FIG. 27 illustrates a handheld device displaying information about aninsurance claim processing system in accordance with some embodiments.

DETAILED DESCRIPTION

An insurer may provide payments when claims are made in connection withan insurance policy, such as a workers' compensation or automobileinsurance policy. Note that embodiments may also be associated withother types of insurance, including long term disability insurance,short term disability insurance, flexible combinations of short and longterm disability insurance, homeowners insurance, property insurance,general liability insurance, commercial insurance, and/or personalinsurance.

Manually determining the complexity of each received insurance claimand/or which claim handler should be assigned to that claim can be timeconsuming and difficult task, especially when there are a substantialnumber of claims to be analyzed. It would therefore be desirable toprovide systems and methods to facilitate the assignment of insuranceclaims to appropriate segments of complexity and/or to claim handlers.FIG. 1 is block diagram of a system 100 according to some embodiments ofthe present invention. In particular, the system 100 includes aninsurance claim processing system 150 that receives information aboutinsurance claims (e.g., by receiving an electronic file from a teamleader, an employer, an employee, an insurance agent, a medical serviceprovider, or a data storage unit 110). According to some embodiments,incoming telephone calls and/or documents from a doctor may be used tocreate information in a claim system 120 which, in turn, can provideinformation to the insurance claim processing system 150. In otherembodiments, the insurance claim processing system 150 may retrieveinformation from a data warehouse 130 (e.g., when the insurance claimprocessing system 150 is associated with an automobile insurance system,some information may be copied from an automobile insurance datawarehouse). In other embodiments, some or all of the information aboutan insurance claim may be received via a claim submission process.

The insurance claim processing system 150 may, according to someembodiments, include segmentation logic 170 that automaticallydetermines an appropriate segment (e.g., based on the likely complexityor liability) for insurance claims (e.g., in accordance withcustomizable configurations parameters 172). This segmentationinformation may then be used by a load balancing and assignment engine180 to select an appropriate claim handler 160 for each insurance claim.According to some embodiments, historical information may be used togenerate appropriate segmentation and/or claim assignment rules to beapplied based on the specific facts of the insurance claim beingprocessed.

The insurance claim processing system 150 might be, for example,associated with a Personal Computers (PC), laptop computer, anenterprise server, a server farm, and/or a database or similar storagedevices. The insurance claim processing system 150 may, according tosome embodiments, be associated with an insurance provider.

According to some embodiments, an “automated” insurance claim processingsystem 150 may facilitate the assignment of insurance claims toappropriate segments and/or claim handlers 160. For example, theinsurance claim processing system 150 may automatically output arecommended claim segment for a received insurance claim (e.g.,indicating that the insurance claim belongs in a “high exposure”segment) which may then be used to facilitate assignment of a claimhandler 160. As used herein, the term “automated” may refer to, forexample, actions that can be performed with little (or no) interventionby a human. Moreover, any of the embodiments described herein may be“dynamically” performed by monitoring parameters and/or automaticallyupdating outputs in substantially real time.

As used herein, devices, including those associated with the insuranceclaim processing system 150 and any other device described herein, mayexchange information via any communication network which may be one ormore of a Local Area Network (LAN), a Metropolitan Area Network (MAN), aWide Area Network (WAN), a proprietary network, a Public SwitchedTelephone Network (PSTN), a Wireless Application Protocol (WAP) network,a Bluetooth network, a wireless LAN network, and/or an Internet Protocol(IP) network such as the Internet, an intranet, or an extranet. Notethat any devices described herein may communicate via one or more suchcommunication networks.

The insurance claim processing system 150 may store information intoand/or retrieve information from the data storage 110. The data storage110 might be associated with, for example, a client, an employer, orinsurance policy and might store data associated with past and currentinsurance claims and/or payments. The data storage 110 may be locallystored or reside remote from the insurance claim insurance claimprocessing system 150. As will be described further below, the datastorage 110 may be used by the insurance claim processing system 150 togenerate predictive models. According to some embodiments, the insuranceclaim processing system 150 communicates a recommended claim processingworkflow (e.g., expedited or normal workflows), such as by transmittingan electronic file to a claim handler 160, a client device, an insuranceagent or analyst platform, an email server, a workflow managementsystem, etc. In other embodiments, the insurance claim processing system150 might output a recommended claim workflow indication to a teamleader who might select a claim handler based on that indication oroverride the indication based on other factors associated with theinsurance claim.

According to some embodiments, the insurance claim processing system 150further includes an anti-fraud wizard 152 (e.g., to help detectinappropriate insurance claims), a Workers' Compensation (“WC”)indemnity reconciliation tool 154 (e.g., to help a claim handler 160comply with various jurisdiction based regulations), a risk transfertool 156 (e.g., to help identify other parties who may have liability inconnection with an insurance claim), and/or a property salvage tool 158(e.g., to help identify situations where value may be identified and/orobtained in connection with an insurance claim). Moreover, the insuranceclaim processing system 150 may transmit information to other devices190 or applications, such as email servers, report generators, calendarapplications, etc. Note that at least some of the tool and otherapplications associated with the insurance claim processing system 150might be incorporated within, or utilize, an electronic spreadsheet,such as the EXCEL® electronic spreadsheet program available fromMICROSOFT®.

Although a single insurance claim processing system 150 is shown in FIG.1, any number of such devices may be included. Moreover, various devicesdescribed herein might be combined according to embodiments of thepresent invention. For example, in some embodiments, the claim insuranceclaim processing system 150 and data storage 110 might be co-locatedand/or may comprise a single apparatus.

FIG. 2 illustrates a method that might be performed by some or all ofthe elements of the system 100 described with respect to FIG. 1according to some embodiments of the present invention. The flow chartsdescribed herein do not imply a fixed order to the steps, andembodiments of the present invention may be practiced in any order thatis practicable. Note that any of the methods described herein may beperformed by hardware, software, or any combination of these approaches.For example, a computer-readable storage medium may store thereoninstructions that when executed by a machine result in performanceaccording to any of the embodiments described herein.

At 202, data may be received indicative of a plurality of insuranceclaims submitted in connection with insurance policies. The insuranceclaims might be associated with, for example, workers' compensationinsurance claims and/or automobile insurance claims. Note that the dataindicative of insurance claims might be received via submitted paperclaims, a telephone call center, and/or an online claim submission website.

At 204, insurance claims may be assigned to claim handlers based, atleast in part, on a segmentation analysis. For example, an insuranceclaim might be recognized as requiring highly complex handling (e.g., aclaim identified as a “longshore” claim) and thus be assigned to a“specialized” segment. As a result, the claim may be assigned to aparticular group of claim handlers who have experience with these typesof insurance claims.

At 206, insurance claims may be analyzed using a fraud detection wizard.The fraud detection wizard may, for example, look for suspiciousinformation, patterns, or values within one or more insurance claims(which, when found, may prompt further investigation. At 208, workers'compensation insurance claims may be verified using a reconciliationtool. For example, different jurisdictions may have differentrecordkeeping requirement and/or penalties associated with workers'compensation claims and the reconciliation tool may help claim handlersprocess such claims in an appropriate manner. At 210, embodiments maylook for potential recoveries using a risk transfer tool. According tosome embodiments, the risk transfer tool might identify other parties(e.g., other insurance companies, employers, etc. who might be liablefor at least a portion of the payments associated with an insuranceclaim). At 212, embodiments may look for potential recoveries using asalvage tool (e.g., the salvage value associated with an automobileaccident) and the insurance claims may be settled.

FIG. 3 is block diagram of a segmentation system 300 according to someembodiments of the present invention. The segmentation system 300includes segmentation logic 370 which may be associated with thesegmentation logic 170 described with respect to FIG. 1. According tosome embodiments, the segmentation logic 370 automatically determines anappropriate segment (e.g., based on the likely complexity or liability)for insurance claims (e.g., in accordance with customizableconfigurations parameters 372). This segmentation information may thenbe transmitted to a load balancing and assignment engine to select anappropriate claim handler for each insurance claim. Note that differenttypes of insurance may be associated with different types ofsegmentation logic. For example, as illustrated in FIG. 3, a workers'compensation insurance claim may be processed using workers'compensation segmentation logic 310 while an insurance claim may beprocessed using automobile insurance segmentation logic 320.

FIG. 4 illustrates a method that might be performed by some or all ofthe elements of the system 300 described with respect to FIG. 3according to some embodiments of the present invention. At 402, data maybe received indicative of a plurality of insurance claims submitted inconnection with insurance policies. The insurance claims might beassociated with, for example, workers' compensation claims, businessinsurance claims, homeowners insurance claims, automobile insuranceclaims, and/or other types of insurance claims. Note that the dataindicative of insurance claims might be received via submitted paperclaims, a telephone call center, an online claim submission web page,etc. in connection with a First Notice Of Loss (“FNOL”). Although someembodiments are described with respect to a newly received insuranceclaim, note that any of the embodiments described herein may beperformed in connection with an insurance claim currently beingprocessed by a claim handler. For example, if information about theinsurance claim changes (e.g., more information about an injuryassociated with the claim becomes available), the system may dynamicallyexecute segmentation logic and automatically adjust the segment that isassociated with the claim. Note that this may, in some cases, result inthe insurance claim being assigned to a different claim handler.

At 404, it may be automatically determined if the insurance claim isassociated with a first type of insurance. In the example of FIG. 4, itis determined if the insurance claim is associated with an automobileinsurance policy. If so, embodiments may analyze the received dataassociated with the insurance claim, in accordance with firstsegmentation logic, to determine a segment classification appropriatefor the insurance claim. In the example of FIG. 4, embodiments mayanalyze the types of damage associated with the insurance claim (e.g.,whether people were injured, automobiles were damaged, windshields werebroken, towing services were required, etc.) at 406. Embodiment may alsoanalyze one or more hard rules associated with other vehicle insurancepolicies, state laws, etc. at 408. As a result of such an analysis, anappropriate segment for the automobile insurance claim may be output at410.

If it was determined that the insurance claim was not associated with anautomobile insurance policy at 404, it may be automatically determiningif an insurance claim is associated with a second type of insurance. Inthe example of FIG. 4, it is determined if the insurance claim isassociated with workers' compensation insurance policy at 412. If so,embodiments may analyze the received data associated with the insuranceclaim, in accordance with second segmentation logic, to determine asegment classification appropriate for the insurance claim. In theexample of FIG. 4, embodiments may analyze the category of the injuryand whether or not the injury occurred while the employee was working at414. Embodiment may also analyze how long the employee has been absentfrom work as a result of the injury at 416. As a result of such ananalysis, an appropriate segment for the workers' compensation claim maybe output at 418.

If the insurance claim is not associated with an automobile or workers'compensation insurance policy, an appropriate segment may be determinedwith other logic at 420, such as logic associated with homeownersinsurance, property insurance, general liability insurance, commercialinsurance, and/or personal insurance. By way of examples, segmentationlogic might evaluate a potential claim liability, an injuryclassification, an injury severity, whether or not people werehospitalized and/or admitted to an intensive care unit. As still otherexample, segmentation logic might evaluate a claimant age (e.g., claimsassociated with people over 60 years old might require specialhandling), co-morbidity information (e.g., claims associated with peoplehaving a Body Mass Index (“BMI”) over a pre-determined threshold valuemight require special handling), an amount of work absence, jurisdictioninformation (e.g., claims from New York might be subject to certainrestrictions), and/or an indication of potential litigation (e.g., if aparticular claimant has legal representation, his or her claim might beassigned to a high risk segment and, as a result, be assigned to specialgroup of claim handlers).

According to some embodiments, segmentation logic may be associated witha segment ranking table. For example, FIG. 5 illustrates a screendisplay 500 of an insurance claim segment ranking table 510 inaccordance with some embodiments. In particular, the table 510 includesthe claim segment description and associated segment rank, with highervalue ranking indicating more complex and/or higher risk insuranceclaims. According to some embodiments, the table is configurable, suchas by letting an operator select 550 a segment and adjust that segment'sposition within the table. For example, the operator might select the“unknown” segment, drag it below the “intermediate” segment, and dropthe segment. As a result, “unknown” might be assigned a rank of “3” andintermediate may be assigned a rank of “2.” Such an adjustment to thetable 510 may result in different claim handlers being selected forthose segments.

FIG. 6 is an example of a method that might be performed according tosome embodiments. At 602, data may be received indicative of a workers'compensation insurance claim. The data might include, or be supplementedto include, for example: a date and time, a claim number, a claim type,a loss date, a benefit state, a claimant name, a claimant date of birth,a cause of injury, a claim description, a body part, an injury, a returnto work date, an indication of whether the claimant was injured doingnormal job duties, an employment status, an indication of whether aninjury resulted in death, an indication of whether the injury requiressurgery, an indication of whether claim involves equipment or machinery,an indication of whether safety equipment was provided, an indication ofwhether the claim is questionable, and/or an indication of whether andinjured worker is represented by an attorney. As still further examples,the data might include a claim description, a type of loss, a body part,an injury, a geographic location, and/or forms and policy endorsementsassociated with the insurance claim and/or insurance policy.

At 604, embodiments may apply injury driven foundation logic todetermine a first segment for the insurance claim (an injury drivenfoundation segment). For example, the insurance claim might beassociated with an injured finger and, as a result, be assigned to a“core” claim segment (with a rank of 1).

At 606, embodiments may apply non-injury driven rules and mechanisms todetermine a second segment for the insurance claim (a non-injury drivensegment). For example, the claimant might be over 60 years old and, as aresult, the insurance claim might be assigned to an “intermediate” claimsegment.

If the second segment (the non-injury driven segment) is not rankedhigher than the first segment (the injury driven foundation segment) at608, the first segment (the injury driven foundation segment) is outputto a load balancing and assignment engine at 610. If the second segment(the non-injury driven segment) is ranked higher than the first segmentat 608, the second segment is output to a load balancing and assignmentengine at 612. That is, the higher ranked segment may always be selectedfor output to the load balancing and assignment engine for processing.The load balancing and assignment engine may then use that higher rankedsegment when selecting a claim handler for the insurance claim.

Note that the embodiments described herein may be implemented using anynumber of different hardware configurations. For example, FIG. 7illustrates a segmentation platform 700 that may be, for example,associated with the systems 100, 300 of FIGS. 1 and 3. The segmentationplatform 700 comprises a processor 710, such as one or more commerciallyavailable Central Processing Units (CPUs) in the form of one-chipmicroprocessors, coupled to a communication device 720 configured tocommunicate via a communication network (not shown in FIG. 7). Thecommunication device 720 may be used to communicate, for example, withone or more claim systems, remote team leaders, load balancing andassignment engines, and/or claim handler devices. The segmentationplatform 700 further includes an input device 740 (e.g., a mouse and/orkeyboard to enter information about segmentation logic) and an outputdevice 750 (e.g., to output an indication of an appropriate segment).

The processor 710 also communicates with a storage device 730. Thestorage device 730 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 730 stores a program712 and/or a segmentation engine 714 for controlling the processor 710.The processor 710 performs instructions of the programs 712, 714, andthereby operates in accordance with any of the embodiments describedherein. For example, the processor 710 may receive data indicative of aplurality of insurance claims submitted in connection with insurancepolicies. The processor 710 may then automatically determine that afirst insurance claim is associated with a first type of insurance andthat a second insurance claim is associated with a second type ofinsurance. The received data associated with the first insurance claimmay be analyzed by the processor 710 in accordance with firstsegmentation logic to determine a first segment classificationappropriate for the first insurance claim. Similarly, the received dataassociated with the second insurance claim may be automatically analyzedthe processor 710 in accordance with second segmentation logic todetermine a second segment classification appropriate for the secondinsurance claim. Indications of the first and second segmentclassifications may then be transmitted (e.g., to a load balancing andassignment engine that automatically selects claim handlers forinsurance claims).

The programs 712, 714 may be stored in a compressed, uncompiled and/orencrypted format. The programs 712, 714 may furthermore include otherprogram elements, such as an operating system, a database managementsystem, and/or device drivers used by the processor 710 to interfacewith peripheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the segmentation platform 700 from another device; or(ii) a software application or module within the segmentation platform700 from another software application, module, or any other source.

In some embodiments (such as shown in FIG. 7), the storage device 730further stores an insurance claim database 800, a cause of injurydatabase 900, an injury database 1000, a configurable parametersdatabase 1100, a jurisdiction database 1200, and a database for otherrules 1300. Examples of databases that may be used in connection withthe segmentation platform 700 will now be described in detail withrespect to FIGS. 8 through 13. Note that the database described hereinis only one example, and additional and/or different information may bestored therein. Moreover, various databases might be split or combinedin accordance with any of the embodiments described herein. For example,the configurable parameters database 1100 and/or the database for otherrules 1300 might be incorporated with the segmentation engine 714.

Referring to FIG. 8, a table is shown that represents the insuranceclaim database 800 that may be stored at the segmentation platform 700according to some embodiments. The table may include, for example,entries identifying insurance claims being processed in accordance withsome embodiments described herein. The table may also define fields 802,804, 806, 808, 810, 812 for each of the entries. The fields 802, 804,806, 808, 810, 812 may, according to some embodiments, specify: a claimidentifier 802, an insurance type 804, a jurisdiction 806, a cause ofinjury 808, an amount of time an employee may miss from work 810, and asegment 812. The insurance claim database 800 may be created andupdated, for example, based on insurance claim information electricallyreceived on a periodic basis.

The claim identifier 802 may be, for example, a unique alphanumeric codeidentifying an insurance claim that has been submitted in connectionwith insurance policy. The insurance type 804 may indicate the type ofinsurance associated with the claim and/or policy. For example, asillustrated in FIG. 8 the insurance claim having a claim identifier of“C_100004” is associated with “homeowners liability insurance.” Thejurisdiction 806 might indicate where the claim was made and/or where aninjury occurred. The cause of injury 808 might indicate what type ofinjury is associated with the insurance claim and the amount of time anemployee may miss from work 810 might indicate, for example, that aclaimant is likely to be out of work for 2 weeks because of his or herinjury. The segment 812 may indicate, for example, the automaticallydetermined segment that reflects the insurance claim's level ofcomplexity, potential liability, etc.

FIG. 9 is a tabular portion of a cause of injury database 900 that maybe stored at the segmentation platform 700 according to someembodiments. The table may include, for example, entries identifyingstandard Claim Description Code (“CDC”) and/or International StatisticalClassification of Diseases and Related Health Problems (“ICD”) codesassociated with insurance claims. The table may also define fields 902,904, 906, 908 for each of the entries. The fields 902, 904, 906, 908may, according to some embodiments, specify: a cause of injuryidentifier 902, a cause of injury 904, an indication of whether newemployees should be excluded 906, and a claim description 908. The causeof injury database 900 may be created and updated, for example, based onpublically available and/or internal information electrically updated ona periodic basis.

The cause of injury identifier 902 may be, for example, a uniquealphanumeric code identifying an injury that might be associated with aninsurance claim and might be based on or associated with the cause ofinjury identifier 808 in the insurance claim database 800. The cause ofinjury 904 might provide more detailed information about the injury andthe indication of whether new employees should be excluded 906 mightindicate, for example, that newly hired claim handlers should not handlethese types of injuries. Finally, the claim description 908 may explainwhat caused the injury (e.g., the cause of injury identifier 902“COI_103” indicates that the claimant slipped or fell from a stairway,escalator, or elevator).

FIG. 10 is a tabular portion of an injury database 1000 that may bestored at the segmentation platform 700 according to some embodiments.The table may include, for example, entries identifying specificinjuries suffered by claimants. The table may also define fields 1002,1004, 1006, 1008 for each of the entries. The fields 1002, 1004, 1006,1008 may, according to some embodiments, specify: an injury identifier1002, a body party 1004, an indication of whether new employees shouldbe excluded 1006, and an injury description 1008. The injury database1000 may be created and updated, for example, based on publicallyavailable and/or internal information electrically updated on a periodicbasis.

The injury identifier 1002 may be, for example, a unique alphanumericcode identifying injuries that might be suffered by claimants. Inparticular, the body party 1004 may indicate where a claimant might behurt and the indication of whether new employees should be excluded 1006might prevent newly hired claim handlers from handling claims with thistype of injury. The injury description 1008 may further describe theinjury (e.g., the injury identifier 1002 “INJ_004” indicates that theclaimant suffered a laceration on his or her facial soft tissue).

FIG. 11 is a tabular portion of a configurable parameters database 1100that may be stored at the segmentation platform 700 according to someembodiments. The table may include, for example, entries identifyingparameters that can be configured by an operator to adjust segmentationlogic. The table may also define fields 1102, 1104, 1106, 1108, 1110 foreach of the entries. The fields 1102, 1104, 1106, 1108, 1110 may,according to some embodiments, specify: a configurable parameteridentifier 1102, an insurance type 1104, an override indication 1106, aparameter description 1108, and a parameter value 1110. The configurableparameter database 1100 may be created and updated, for example, by anadministrator, programmer, and/or operator of an insurance claimprocessing system.

The configurable parameter identifier 1102 may be, for example, a uniquealphanumeric code identifying a parameter that may be used to controlsegmentation for a particular insurance type 1104. The overrideindication 1106 may indicate whether or not this parameter can bechanged by particular parties. The parameter description 1108 andassociated parameter value 1110 may define, for example, logic or a rulethat can be used when performing segmentation on an insurance claim. Forexample, the configurable parameter 1102 of “CP_001” indicates that forworkers' compensation insurance claims claimants over 60 years old asegment of “core” is the lowest ranked segment that is allowed to beoutput for that insurance claim. Moreover, by adjusting the parametervalue 1110 in the database 1100, this age can be adjusted.

FIG. 12 is a tabular portion of a jurisdiction database 1200 that may bestored at the segmentation platform 700 according to some embodiments.The table may include, for example, entries identifying jurisdictionsthat may be associated with insurance claims. The table may also definefields 1202, 1204, 1206 for each of the entries. The fields 1202, 1204,1206 may, according to some embodiments, specify: a jurisdiction 1202,an insurance type 1204, and a rule 1206. The jurisdiction database 1200may be created and updated, for example, by an administrator,programmer, and/or operator of an insurance claim processing system.

The jurisdiction 1202 may be, for example, a unique alphanumeric codeidentifying a jurisdiction (e.g., a stated) for a particular insurancetype 1204. Moreover, the rule 1206 may define how insurance claimsshould be handled within that jurisdiction 1202. For example, in thejurisdiction 1202 of Connecticut, workers' compensation insurance claimsmight be subject to a statutory minimum average weekly wage of $400.

FIG. 13 is a tabular portion of a database of other rules 1300 that maybe stored at the segmentation platform 700 according to someembodiments. The table may include, for example, entries identifyingrules that have been defined to be applied within segmentation logic.The table may also define fields 1302, 1304 for each of the entries. Thefields 1302, 1304 may, according to some embodiments, specify: a ruleidentifier 1302 and the rule 1304. The database of other rules 1300 maybe created and updated, for example, by an administrator, programmer,and/or operator of an insurance claim processing system.

The rule identifier 1302 may be, for example, a unique alphanumeric codeidentifying the rule 1304. For example, the rule identifier 1302 of“R_001” indicates that for workers' compensation claims, if the claimtype is “longshore” then the output segment should be “specialized.” Asa result, those insurance claims may be assigned to a claim handlerwithin a group of handlers having experience with those types ofinsurance claims.

The information in the databases described with respect to FIGS. 8through 13 may be used by a claim processing system (e.g., viasegmentation logic within the system) to assign an appropriate segmentto each insurance claim. For example, FIG. 14 illustrates a screendisplay 1400 of a segmentation report in accordance with someembodiments. In particular, the report lists insurance claims along withthe segment 1410 that has been assigned to each claim. These segments1410 may then be used to assign the claims to appropriate claimhandlers.

FIG. 15 is block diagram of a load balancing and assignment system 1500according to some embodiments of the present invention. The system 1500includes a load balancing and assignment system 1500 includes a loadbalancing and assignment engine 1580 that may be associated with theload balancing and assignment system engine 180 of FIG. 1. The loadbalancing and assignment engine 180 may receive indications of segmentclassifications from segmentation logic. Moreover, a load balancingplatform 1582 and/or claim handler assignment platform 1584 may use thesegmentation information to assign insurance claims to appropriate claimhandlers 1560. A claim database 1600 may store information aboutinsurance claims that have been received by the insurance claimprocessing system, and a claim handler database 1700 may storeinformation about the claim handlers 1560.

According to some embodiments, the load balancing platform 1582 may helpensure that various claim handlers 1560 work on an appropriate number ofinsurance claims at any given time. That is, the load balancing platform1582 may select a claim handler 1560 based at least in part on the totalnumber of insurance claims that are currently assigned to various claimhandlers 1560. Moreover, according to some embodiments each claimhandler 1560 is associated with a “load factor” that indicates arelative workload (e.g., total number of insurance claims) that might beappropriate for that particular handler 1560. For example, a newly hiredclaim handler 1560 might be expected to handle fewer insurance claims ascompared to a more experience handler 1560.

Moreover, in some embodiments the load balancing platform may receive anindication of employee availability from a separate human resourcesplatform 1590. The indication of employee availability may reflect, forexample, paid time off, sick days, vacation, unpaid absences, anindication that an employee is temporarily away from his or herworkstation, or a specific limitation associated with a specificemployee (e.g., a particular claim handler might only work four hoursper day). According to some embodiments, the load balancing platform1582 may select a claim handler 1560 based at least in part on scheduledpaid time off associated with each claim handler 1560. For example, aclaim handler 1560 who is about to take a one week vacation might beexpected to handle fewer insurance claims as compared to other claimhandlers 1560. Still further, according to some embodiments the loadbalancing platform 1582 may select a claim handler 1560 based at leastin part on a request for more insurance claims received from that claimhandler 1560. For example, a claim handler 1560 who indicates he or sheis available and able to take on additional cases may be more likely tobe assigned a new insurance claim as compared to other claim handlers1560.

FIG. 16 is a tabular portion of a claim database 1600 that may be storedat the load balancing and assignment engine 1500 according to someembodiments. The table may include, for example, entries identifyingclaims being handled by the insurance claim processing system. The tablemay also define fields 1602, 1604, 1606, 1608 for each of the entries.The fields 1602, 1604, 1606, 1608 may, according to some embodiments,specify: a claim identifier 1602, an insurance type 1604, a segment1606, and an assigned claim handler identifier 1608. The claim database1600 may be created and updated, for example, based on informationelectrically received from segmentation logic and/or generated by a loadbalancing and assignment engine on a periodic basis.

The claim identifier 1602 may be, for example, a unique alphanumericcode identifying a claim being handled by the insurance claim processingsystem in connection with the insurance type 1604. The segment 1606 mayindicate, for example, the complexity and/or level of risk for the claimas was automatically determined by segmentation logic. The assignedclaim handler identifier 1608 may indicate, for example, a particularperson who has been assigned to work on that insurance claim.

FIG. 17 is a tabular portion of a claim handler database 1700 that maybe stored at the load balancing and assignment engine 1500 according tosome embodiments. The table may include, for example, entriesidentifying people may be selected to handle incoming insurance claims.The table may also define fields 1702, 1704, 1706, 1708, 1710, 1712 foreach of the entries. The fields 1702, 1704, 1706, 1708, 1710, 1712 may,according to some embodiments, specify: a claim handler identifier 1702,contact information 1704, a load factor 1706, a number of current claims1708, upcoming paid time off information 1710, and a request moreindication 1712. The claim handler database 1700 may be created andupdated, for example, based on information entered by an operator orelectronically received from an insurer's human resources department ona periodic basis.

The claim handler identifier 1702 may be, for example, a uniquealphanumeric code identifying a person who might be available to work onincoming insurance claims an may be based on or associated with theassigned claim handler identifier 1612 in the claim database 1600. Thecontact information 1704 might be a telephone number, email or IPaddress, etc. indicating how communication with that person might beestablished.

The load factor 1706 and number of current claims 1708 being worked onby the claim handler may be used to balance workloads between claimhandlers. In particular, a higher load factor 1706 may indicate that theclaim handler is able to handle more claims as compared to handlers withlower load factors 1706 (with a load factor of 100% being average in theexample of FIG. 17). In particular, an adjusted load L_(ADJUSTED) may becalculated for a claim handler as follows:

$L_{ADJUSTED}{= \frac{{NumberOfClaims}_{Current}}{LF}}$

where NumberOfClaims_(current) equals the current claims 1708 and LFequals the load factor 1706 in the claim handler database. TheL_(ADJUSTED) may then be used to compare workloads between claimhandlers and assign new insurance claims as appropriate. In the exampleof FIG. 17, note that claim handler “H_101” is currently handling moreclaims 30 claims) as compared to claim handler “H_102” (25 claims). Anew insurance claim, however, might be more likely to be assigned to“H_101” because his or her adjusted load (30 divided by 100=0.3) islower than the adjusted load calculated for “H_102” (25 divided by80=0.3125).

The upcoming paid time off information 1710 may indicate, for example,whether the handler associated with the claim handler identifier 1702has an upcoming vacation scheduled, and the request more indication 1712may indicate whether or not that particular handler has indicated thathe or she is currently looking to take on additional work.

Thus, the information in the database of FIGS. 16 and 17 may be used toassign new insurance claims to claim handlers. For example, FIG. 18 isan example of a method 1800 that might be performed according to someembodiments. At 1802, data indicative of an insurance claim submitted inconnection with an insurance policy may be received along withautomatically generated segmentation information. At 1804, a claimhandler database may be accessed to determine a set of all eligibleclaim handlers for that insurance claim. The set may be based on, forexample, the segmentation information and other information about theclaim (e.g., the claim might require a Spanish speaking insurance claimhandler).

At 1806, handlers with current or upcoming paid time off (e.g.,vacations) may be removed from the set of eligible claim handlers. At1808, an adjusted load may be calculated for each remaining handler bydividing the number of claims he or she is currently working on by hisor her load factor. At 1810, the insurance claim may then be assigned toone of the remaining handlers based on the adjusted workloads. Accordingto some embodiments, an insurance claim processing system may also takeinto consideration those handlers who have requested more work (if any)and/or use a round robin algorithm to resolve situation where multiplehandlers are equally suitable to receive an insurance claim.

FIG. 19 is a graph 1900 illustrating overall numbers of insurance claimsthat are currently being processed by claim handlers via an expeditedworkflow in accordance with some embodiments. The solid bars in FIG. 19indicate how many insurance claims each of three handlers are currentlyworking on. That is, a first claim handler is working on fewer claims1912 as compared to a second claim handler 1914, and a third claimhandler is worker the fewest claims 1916. Thus, a newly receivedinsurance claim might be assigned to the third claim handler (assuminghe or she is appropriate in view of the new claim's segment). However, apaid time off adjustment might be made for the third claim handlerbecause he or she is planning on taking a two week vacation (that is,the paid time off information can be used for more than a simple“removal” of the handler as was described in FIG. 18). As a result ofthis adjustment, the third claim handler has the highest workload (asillustrated by the dotted line). Moreover, a load factor adjustment hasbeen made for the second claim handler's workload (as illustrated by thedashed line), perhaps because he or she is more experienced and/orproductive as compared to the other handlers. As a result, the secondclaim handler has the lowest caseload (as illustrated by the dashedline) and would therefore receive the next insurance claim that isreceived by the insurance claim processing system. According to someembodiments, after selection of a claim handler for an insurance claim,feedback performance data may be evaluated (e.g., queue lengths, claimprocessing times, quality reviews, etc.) and, as a result of theevaluation, that insurance claim may be re-assigned to a different claimhandler.

In some cases, insurance claims may be assigned to claim handlers basedon information other than case loads and vacation time. For example,FIG. 20 illustrates a screen display 2000 of an assignment preferencestable for a particular type of insurance line of business 2010 (e.g.,automobile, property, general liability, or workers' compensation) inaccordance with some embodiments. The assignment preferences table maylet business users configure claim handler assignment for particularoffices 2020 (e.g., at different geographical locations such that claimhandlers may be associated with one of a plurality of claim handleroffices, at least one of the claim handler offices being remote fromother claim handler offices) and/or claim segments 2022 (i.e., core,intermediate, etc.). According to some embodiments, overflow rules maylet business users set an overflow threshold. The system may allow, forexample, a different overflow threshold on Mondays 2024 to account forthe addition volume that may have accumulated over the weekend. When allusers in an office with the respective segment attribute meet or exceedthe threshold (based on the number claims assigned that day), thestandard overflow threshold 2026 is met.

The table may let business users set either an overflow segment 2028 oran overflow queue 2030, which instructs the system what to do when theoverflow threshold is met. In the case of an overflow segment 2028, thesystem may add users with the defined overflow segment 2022 to the groupof users eligible for assignment when the overflow threshold 2026 ismet. Note the system may load balances amounts for both segments suchthat if the users of the initial segment are less busy the system willlikely continue to assign claims to the initial segment. On the otherhand, if users of the overflow segment are less busy the system willlikely assign claims to the overflow segment.

In the case of an overflow queue 2030, the system may assigns claim withthe respective claim segment to the specified overflow queue 2030 whenoverflow threshold 2026 is met. Thus, the specified queue may crossoffices 2020 and even lines of business 2010.

According to some embodiments, the display 2000 lets business userstoggle assignments directly to a user or directly to a queue. When thedirect assign queue 2032 is blank, the system may assign claims with therespective claim segment 2022 directly to a claim handler using the loadbalancing algorithm. On the other hand, when a queue 2032 is specified,the system may assign claims with the respective claim segment 2022 tothe specified queue 2023. Again, note that the specified queue 2032 cancross offices 2020 and lines of business 2010.

According to some embodiments, the table lets business users assignclaims to specified queues at night 2024 and/or on the weekends. Thenight 2034 timeframe may be defined by the business. When the night 2034and/or the weekend queues are specified, the system may assign claimswith the respective segment 2022 to the specified queue 2034 when claimsare reported within the business definition of night (or on theweekend). Because automated assignment occurs continuously, thiscapability may let the business manually assign claims to adjusters whoare actually at work and/or are working after hours.

In some embodiments, users may designate an exception queue 2036 forexception scenarios. When the system cannot find an eligible user, theclaim is assigned to the designated exception queue 2036. This mayoccur, for example, if the system is unable to find a user with therequired attributes or if all users with the required attributes are onvacation.

According to some embodiments, the system determines if users areeligible for assignment by comparing the claim details with userprofiles. The system may determine eligibility based on three attributecategories: segment attributes, expertise attributes, and stateattributes. The segment attributes may be primary determining factor,and a single user may be qualified to handle multiple segments. Theexpertise attributes (e.g., commercial claims, personal claims, claimplus claims, non-claim plus claims, employee claims, personal toysclaims, uninsured motorist claims, employer's liability, loss assessmentclaims, identity theft claims, asbestos claims, etc.) may be used todesignate a special skill or ability to handle special claim types.Expertise attributes may vary by line of business. The state attributes(e.g., AK, CA, DE, FL, NY, CT, etc.) may indicate that most adjustershandle a subset of states within their respective office based onbusiness needs and/or state licensing requirements.

In some embodiments, automated re-segmentation may be performed. Notethat the system may derive a claim segment based on claim details atFNOL and post-FNOL. The FNOL segmentation and post-FNOL re-segmentationmay use the same logic, however there may be a number of fields thatonly become available post-FNOL (e.g., a claimant's height & weight).When one or more of the following data elements are adjusted post-FNOL,the system may re-run the segmentation logic and automaticallyre-segment the claim if warranted: loss date, loss state, claimdescription code, injury, matters, and/or reserves. If the segment isdifferent and the claim requires a higher level resource based on thesegmentation hierarchy for each line of business, the system maygenerate a re-assignment activity for the current adjuster's teamleader.

Certain insurance policies and/or insured may be associated withparticular claim handlers. In such cases, the system may let businessusers assign one or more handlers to a specific account at the policylevel. For example, FIG. 21 illustrates a screen display 2100 of adedicated claim handler manager in accordance with some embodiments. Theuser may select an insurance policy identifier 2110 or insured to beassociated with a team of dedicated claim handler. Note that if aparticular insured has both automobile and general liability policieswith an insurer, a different handler or group of handlers might beassigned to that account based on each line of business. According tosome embodiments, a user may define a period 2120 during which thededicated handlers will receive insurance claims. The user may also add2130 handlers 2150 to the team for that insurance policy as appropriate.According to some embodiments, the user may identify some handlers as“Primary” 2140 handlers. In this case, the system may assign all claimsfor that account to the primary handler. If the primary is not available(e.g., he or she is sick or on vacation), the system may instead assigninsurance claims to one or more backup handlers as defined on thedisplay 2100 (e.g., primary 2140 equals “N”). According to someembodiments, a dedicated team might be associated with an insurancepolicy such that if more than one handler is dedicated to anaccount/policy and no primary is specified, the system may interpretthis as meaning that specified handlers are dedicated and load balanceclaims for the account evenly amongst the group.

Some embodiment may provide for catastrophe management. For example,FIG. 22 illustrates a screen display 2200 of a catastrophe managementtable in accordance with some embodiments. In particular, the display2200 lets business users set catastrophe routing preferences for eachcatastrophe. The display 2200 may, for example, let the user a user adda catastrophe 2210 by defining a status 2222 (e.g., active or inactive),a catastrophe name 2222, an identifier 2224, a begin date 2228, and anend date 2228. According to some embodiments, all claims associated witha catastrophe are load balanced amongst eligible adjusters in aCatastrophe Claim Office (“CCO”). In some embodiments, claims associatedwith a catastrophe are assigned to the field the same way claims notassociated with the catastrophe would be assigned. That is, claims maybe load balanced among eligible adjusters in the field claim offices vs.(instead of the CCO) the CAT claim office. According to someembodiments, all claims associated with a catastrophe are load balancedamong eligible adjusters in both the CCO and the field claim offices.

Thus, segmentation logic and/or a load balancing and assignment enginemay facilitate the processing of insurance claims. According to someembodiment, a predictive model may be used in connection with thesegmentation and/or assignment processes. As used herein, the phrase“predictive model” might refer to, for example, any of a class ofalgorithms that are used to understand relative factors contributing toan outcome, estimate unknown outcomes, discover trends, and/or makeother estimations based on a data set of factors collected across priortrials. Note that a predictive model might refer to, but is not limitedto, methods such as ordinary least squares regression, logisticregression, decision trees, neural networks, generalized linear models,and/or Bayesian models. The predictive model might be trained withhistorical claim transaction data, and be applied to current claimtransactions to determine how the current claim transactions should behandled. Both the historical claim transaction data and datarepresenting the current claim transactions might include, according tosome embodiments, indeterminate data or information extracted therefrom.For example, such data/information may come from narrative and/ormedical text notes associated with a claim file.

Features of some embodiments associated with a predictive model will nowbe described by first referring to FIG. 23. FIG. 23 is a partiallyfunctional block diagram that illustrates aspects of a computer system2300 provided in accordance with some embodiments of the invention. Forpresent purposes it will be assumed that the computer system 2300 isoperated by an insurance company (not separately shown) for the purposeof referring certain claims to insurance claim workflows and/or handlersas appropriate.

The computer system 2300 includes a data storage module 2302. In termsof its hardware the data storage module 2302 may be conventional, andmay be composed, for example, by one or more magnetic hard disk drives.A function performed by the data storage module 2302 in the computersystem 2300 is to receive, store and provide access to both historicalclaim transaction data (reference numeral 2304) and current claimtransaction data (reference numeral 2306). As described in more detailbelow, the historical claim transaction data 2304 is employed to train apredictive model to provide an output that indicates how a claim shouldby assigned to claim handler, and the current claim transaction data2306 is thereafter analyzed by the predictive model. Moreover, as timegoes by, and results become known from processing current claimtransactions, at least some of the current claim transactions may beused to perform further training of the predictive model. Consequently,the predictive model may thereby adapt itself to changing claimpatterns.

Either the historical claim transaction data 2304 or the current claimtransaction data 2306 might include, according to some embodiments,determinate and indeterminate data. As used herein and in the appendedclaims, “determinate data” refers to verifiable facts such as the dateof birth, age or name of a claimant or name of another individual or ofa business or other entity; a type of injury, accident, sickness, orpregnancy status; a medical diagnosis; a date of loss, or date of reportof claim, or policy date or other date; a time of day; a day of theweek; a vehicle identification number, a geographic location; and apolicy number.

As used herein and in the appended claims, “indeterminate data” refersto data or other information that is not in a predetermined formatand/or location in a data record or data form. Examples of indeterminatedata include narrative speech or text, information in descriptive notesfields and signal characteristics in audible voice data files.Indeterminate data extracted from medical notes or accident reportsmight be associated with, for example, an amount of loss and/or detailsabout how an accident occurred.

The determinate data may come from one or more determinate data sources2308 that are included in the computer system 2300 and are coupled tothe data storage module 2302. The determinate data may include “hard”data like the claimant's name, date of birth, social security number,policy number, address; the date of loss; the date the claim wasreported, etc. One possible source of the determinate data may be theinsurance company's policy database (not separately indicated). Anotherpossible source of determinate data may be from data entry by theinsurance company's claims intake administrative personnel.

The indeterminate data may originate from one or more indeterminate datasources 2310, and may be extracted from raw files or the like by one ormore indeterminate data capture modules 2312. Both the indeterminatedata source(s) 2310 and the indeterminate data capture module(s) 2312may be included in the computer system 2300 and coupled directly orindirectly to the data storage module 2302. Examples of theindeterminate data source(s) 2310 may include data storage facilitiesfor document images, for text files (e.g., claim handlers' notes) anddigitized recorded voice files (e.g., claimants' oral statements,witness interviews, claim handlers' oral notes, etc.). Examples of theindeterminate data capture module(s) 2312 may include one or moreoptical character readers, a speech recognition device (i.e.,speech-to-text conversion), a computer or computers programmed toperform natural language processing, a computer or computers programmedto identify and extract information from narrative text files, acomputer or computers programmed to detect key words in text files, anda computer or computers programmed to detect indeterminate dataregarding an individual. For example, claim handlers' opinions may beextracted from their narrative text file notes.

The computer system 2300 also may include a computer processor 2314. Thecomputer processor 2314 may include one or more conventionalmicroprocessors and may operate to execute programmed instructions toprovide functionality as described herein. Among other functions, thecomputer processor 2314 may store and retrieve historical claimtransaction data 2304 and current claim transaction data 2306 in andfrom the data storage module 2302. Thus the computer processor 2314 maybe coupled to the data storage module 2302.

The computer system 2300 may further include a program memory 2316 thatis coupled to the computer processor 2314. The program memory 2316 mayinclude one or more fixed storage devices, such as one or more hard diskdrives, and one or more volatile storage devices, such as RAM devices.The program memory 2316 may be at least partially integrated with thedata storage module 2302. The program memory 2316 may store one or moreapplication programs, an operating system, device drivers, etc., all ofwhich may contain program instruction steps for execution by thecomputer processor 2314.

The computer system 2300 further includes a predictive model component2318. In certain practical embodiments of the computer system 2300, thepredictive model component 2318 may effectively be implemented via thecomputer processor 2314, one or more application programs stored in theprogram memory 2316, and data stored as a result of training operationsbased on the historical claim transaction data 2304 (and possibly alsodata resulting from training with current claims that have beenprocessed). In some embodiments, data arising from model training may bestored in the data storage module 2302, or in a separate data store (notseparately shown). A function of the predictive model component 2318 maybe to determine appropriate segmentation logic and/or claim assignmentprocess. The predictive model component may be directly or indirectlycoupled to the data storage module 2302.

The predictive model component 2318 may operate generally in accordancewith conventional principles for predictive models, except, as notedherein, for at least some of the types of data to which the predictivemodel component is applied. Those who are skilled in the art aregenerally familiar with programming of predictive models. It is withinthe abilities of those who are skilled in the art, if guided by theteachings of this disclosure, to program a predictive model to operateas described herein.

Still further, the computer system 2300 includes a model trainingcomponent 2320. The model training component 2320 may be coupled to thecomputer processor 2314 (directly or indirectly) and may have thefunction of training the predictive model component 2318 based on thehistorical claim transaction data 2304. (As will be understood fromprevious discussion, the model training component 2320 may further trainthe predictive model component 2318 as further relevant claimtransaction data becomes available.) The model training component 2320may be embodied at least in part by the computer processor 2314 and oneor more application programs stored in the program memory 2316. Thus thetraining of the predictive model component 2318 by the model trainingcomponent 2320 may occur in accordance with program instructions storedin the program memory 2316 and executed by the computer processor 2314.

In addition, the computer system 2300 may include an output device 2322.The output device 2322 may be coupled to the computer processor 2314. Afunction of the output device 2322 may be to provide an output that isindicative of (as determined by the trained predictive model component2318) particular segmentation information for the current claimtransactions. The output may be generated by the computer processor 2314in accordance with program instructions stored in the program memory2316 and executed by the computer processor 2314. More specifically, theoutput may be generated by the computer processor 2314 in response toapplying the data for the current claim transaction to the trainedpredictive model component 2318. The output may, for example, be atrue/false flag or a number within a predetermined range of numbers. Insome embodiments, the output device may be implemented by a suitableprogram or program module executed by the computer processor 2314 inresponse to operation of the predictive model component 2318.

Still further, the computer system 2300 may include a segmentation,assignment, and load balancing module 2324. The segmentation,assignment, and load balancing module 2324 may be implemented in someembodiments by a software module executed by the computer processor2314. The segmentation, assignment, and load balancing module 2324 mayhave the function of directing workflow based on the output from theoutput device. Thus the segmentation, assignment, and load balancingmodule 2324 may be coupled, at least functionally, to the output device2322. In some embodiments, for example, the segmentation, assignment,and load balancing module 2324 may direct workflow by referring, to aclaim handler 2326, current claim transactions analyzed by thepredictive model component 2318 and found to be associated with one ormore claim segments. In some embodiments, these current claimtransactions may be referred to case manager 2328 who is associated withthe claim handler 2326. The claim handler 2326 may be a part of theinsurance company that operates the computer system 2300, and the casemanager 2328 might be an employee of the insurance company.

FIG. 24 is another block diagram that presents a computer system 2400 ina somewhat more expansive or comprehensive fashion (and/or in a morehardware-oriented fashion). The computer system 2400, as depicted inFIG. 24, includes a “segmentation, assignment, and load balancingengine” 2401 to automatically and selectively assess newly receivedinsurance claims for the insurance company. As seen from FIG. 24, thecomputer system 2400 may further include a conventional datacommunication network 2402 to which the segmentation, assignment, andload balancing engine 2401 is coupled.

FIG. 24 also shows, as parts of computer system 2400, data inputdevice(s) 2404 and data source(s) 2406, the latter (and possibly alsothe former) being coupled to the data communication network 2402. Thedata input device(s) 2404 and the data source(s) 2406 may collectivelyinclude the devices 2308, 2310 and 2312 discussed above with referenceto FIG. 23. More generally, the data input device(s) 2404 and the datasource(s) 2406 may encompass any and all devices conventionally used, orhereafter proposed for use, in gathering, inputting, receiving and/orstoring information for insurance company claim files.

Still further, FIG. 24 shows, as parts of the computer system 2400,personal computer 2408 assigned for use by one or more physicians (whomay be associated with the insurance company's long term disabilityinsurance program), automobile shop computer 2409 (e.g., to transmitrepair estimates), and personal computers 2410 assigned for use by casemanagers (who might also be associated with team leaders and/or claimhandlers the long term disability insurance program). The personalcomputers 2408, 2409, 2410 may be coupled to the data communicationnetwork 2402.

Also included in the computer system 2400, and coupled to the datacommunication network 2402, is an electronic mail server computer 2412.The electronic mail server computer 2412 provides a capability forelectronic mail messages to be exchanged among the other devices coupledto the data communication network 2402. Thus the electronic mail servercomputer 2412 may be part of an electronic mail system included in thecomputer system 2400. The computer system 2400 may also be considered toinclude further personal computers (not shown), including, e.g.,computers which are assigned to individual claim handlers or otheremployees of the insurance company.

According to some embodiments, the segmentation, assignment, and loadbalancing engine 2401 uses a predictive model to facilitate aprovisioning of claim handlers. Note that the predictive model might bedesigned and/or trained in a number of different ways. For example, FIG.25 is a flow chart illustrating how a predictive model might be createdaccording to some embodiments. At 2502, data to be input to thepredictive model may be analyzed, scrubbed, and/or cleaned. This processmight involve a broad review of the relevant variables that may beincluded in the sample data. Variables might be examined for thepresence of erroneous values, such as incorrect data types or valuesthat don't make sense. Observations with such “noisy” data or missingdata may be removed from the sample. Similarly, any data points thatrepresent outliers may also be managed.

At 2504, a data reduction process might be performed. This might occur,for example, between variables in the data sample and/or within specificvariables. According to some embodiments, certain variables may beassociated with one another and the number of these variables may bereduced. For example, it might be noted that back injuries should not behandled via an expedited workflow process. Within certain variables, theraw values may represent a level of information that is too granular.These raw values might then be categorized to reduce the granularity. Agoal of the data reduction process may be to reduce the dimensionalityof the data by extracting factors or clusters that may account for thevariability in the data.

At 2506, any necessary data transformations may be performed.Transformations of dependent and/or independent variables in statisticalmodels can be useful for improving interpretability, model fit, and/oradherence to modeling assumptions. Some common methods may includenormalizations of variables to reduce the potential effects of scale anddummy coding or other numeric transformations of character variables.

Once these steps are complete, the predictive model may be developed at2508. Depending on the nature of the desired prediction, variousmodeling techniques may be utilized and compared. The list ofindependent variables may be narrowed down using statistical methods aswell as business judgment. Lastly, the model coefficients and/or weightsmay be calculated and the model algorithm may be completed. For example,it might be determined that back injuries require a high degree ofmanagement (and thus, according to some embodiments, a back injury mightbe weighted more as compared to a shoulder injury and thus be morelikely to end up in a high complexity segment).

Note that many different types of data might be used to create,evaluate, and/or use a predictive model. For example, FIG. 26 is a blockdiagram of a system 2600 illustrating inputs to a predictive model 2610according to some embodiments. In this example, the predictive model2610 might receive information about prior insurance claims 2620 (e.g.,historical data). Moreover, the predictive model 2610 might receivemonetary information about claims 2630 (e.g., a total amount of paymentsmade in connection with a claim) and/or demographic information 2640(e.g., the age or sex of a claimant). According to some embodiments,claim notes 2650 are input to the predictive model 2610 (e.g., andkeywords may be extracted from the notes 2650). Other types ofinformation that might be provided to the predictive model 2610 includemedical bill information 2660 (e.g., including information about medicalcare that was provided to a claimant), disability details 2670 (e.g.,which part or parts of the body have been injured), and employment data2680 (e.g., an employee's salary or how long an employee has worked foran employer).

The predictive model 2610, in various implementation, may include one ormore of neural networks, Bayesian networks (such as Hidden Markovmodels), expert systems, decision trees, collections of decision trees,support vector machines, or other systems known in the art foraddressing problems with large numbers of variables. Preferably, thepredictive model(s) are trained on prior data and outcomes known to theinsurance company. The specific data and outcomes analyzed varydepending on the desired functionality of the particular predictivemodel 2610. The particular data parameters selected for analysis in thetraining process are determined by using regression analysis and/orother statistical techniques known in the art for identifying relevantvariables in multivariable systems. The parameters can be selected fromany of the structured data parameters stored in the present system,whether the parameters were input into the system originally in astructured format or whether they were extracted from previouslyunstructured text.

Applicants have discovered that embodiments described herein may beparticularly useful in connection with the insurance policies describedherein. Note, however, that other types of insurance may also beassociated with embodiments described herein. Moreover, the displays500, 1400 illustrated with respect to FIGS. 5 and 14 are only providedas examples, and embodiments may be associated with any other types ofuser interfaces. For example, FIG. 27 illustrates a handheld claimsegmentation and assignment display 2700 according to some embodiments.In this particular user display 2700, a list of claim handlers 2710assigned to various insurance claims is provided. Moreover, a user mightmodify 2720 one or more of the assignments as appropriate.

Note that the present invention provides significant technicalimprovements to insurance claim segmentation and/or assignments to claimhandlers. The present invention is directed to more than merely acomputer implementation of a routine or conventional activity previouslyknown in the industry as it significantly advances the technicalefficiency, access and/or accuracy of insurance claim segmentationand/or assignments to claim handlers by implementing a specific newmethod and system as defined herein. The present invention is a specificadvancement in the area of insurance claim segmentation and/orassignments to claim handlers by providing technical benefits in dataaccuracy, data availability and data integrity and such advances are notmerely a longstanding commercial practice. The present inventionprovides improvement beyond a mere generic computer implementation as itinvolves the processing and conversion of significant amounts of data ina new beneficial manner as well as the interaction of a variety ofspecialized insurance, client and/or vendor systems, networks andsubsystems. For example, in the present invention tens of thousandsinsurance claims may be analyzed and automatically assigned to anappropriate claim handler.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

What is claimed is:
 1. A system, comprising: a communication device toreceive claim data for a plurality of claims submitted in connectionwith policies of a first coverage type and a second coverage type; acomputer storage unit for receiving, storing, and providing the claimdata for the plurality of claims; a claims processing computer systemcoupled to the computer storage unit, the claims processing computersystem including: a segmentation platform processor in communicationwith the computer storage unit, wherein the segmentation platformprocessor is configured for automatically determining, for each claim ofthe plurality of claims, a segment classification based on applicationof segmentation logic applicable to the claim; and a load balancing andassignment engine configured for: receiving a segment classificationcomprising one of a first segment classification indication or a secondsegment classification indication for each of the plurality of claimsfrom the segmentation platform processor; accessing a claim handlerdatabase to identify eligible claim handlers for each of the pluralityof claims based on the one of the first segment classificationindication or the second segment classification indication for each ofthe plurality of claims; determining a load for each of the eligibleclaim handlers for each of the plurality of claims; automaticallyselecting, for each of the plurality of claims based on the determinedload for each of the corresponding eligible claim handlers for theclaim, assignment of each claim to one of the eligible claim handlersand a case manager; and transmitting an electronic file for each of theplurality of claims to the assigned one of the claim handler and thecase manager.
 2. The system of claim 1, further comprising a claimintake computer system configured for receiving the claim datacorresponding to the plurality of claims of the first coverage type andthe second coverage type, wherein the claims processing computer systemis further coupled to the claim intake computer system.
 3. The system ofclaim 1, wherein the segmentation logic comprises first segmentationlogic applicable to claims of the first coverage type and secondsegmentation logic applicable to claims of the second coverage type. 4.The system of claim 3, wherein automatically determining the segmentclassification for each claim of the plurality of claims comprises:analyzing, for each of the claims associated with the first coveragetype, the received data associated with the claim, in accordance withthe first segmentation logic, to determine a first segmentclassification appropriate for the claim; and analyzing, for each of theclaims associated with the second coverage type, the received dataassociated with the claim, in accordance with the second segmentationlogic different from the first segmentation logic, to determine a secondsegment classification appropriate for the claim.
 5. The system of claim4, wherein the claims processing computer system includes a configurablesegment ranking table for configuring the first segmentation logic; andwherein said analyzing the received data associated with the claim, inaccordance with the first segmentation logic, comprises: determining aninjury driven foundation segment; applying non-injury driven rules todetermine a non-injury driven segment; and selecting one of the injurydriven foundation segment and the non-injury driven segment based on theconfigurable segment ranking table.
 6. The system of claim 1, whereinthe load balancing and assignment engine being configured fordetermining the load for each of the eligible claim handlers for each ofthe plurality of claims comprises: accessing data indicative of a numberof other claims currently assigned to each of the eligible claimhandlers; and determining an adjusted load based on the accessed dataindicative of the number of other claims currently assigned and one ormore of data indicative of paid time off information for each of theeligible claim handlers and a load factor indicative of a claim-handlingability of the eligible claim handlers.
 7. The system of claim 1,wherein at least one of the segmentation platform processor and the loadbalancing and assignment engine are implemented by a predictive modeltrained with historical claim information; wherein the predictive modelis associated with at least one of: (i) a computer-implemented neuralnetwork, (ii) a computer-implemented Bayesian network, (iii) acomputer-implemented Hidden Markov model, (iv) a computer-implementedexpert system, (v) a computer-implemented decision tree, (vii) acomputer-implemented collection of decision trees, (viii) acomputer-implemented support vector machine, and (ix)computer-implemented weighted factors.
 8. The system of claim 7, furthercomprising a model training component computer processor configured to:receive a plurality of historical data sets comprising the historicalclaim information from the computer storage unit; apply one or both of avariable association data reduction process and a granularity reductionprocess to the plurality of historical data sets to generate reducedhistorical data sets and reduce a dimensionality of the plurality ofhistorical data sets; and train, based upon the reduced historical datasets, a predictive model to determine segment classifications based uponclaims data.
 9. A computer-implemented method comprising: receiving, bya communication device data indicative of a plurality of claimssubmitted in connection with policies providing at least a firstcoverage type and a second coverage type different from the firstcoverage type; storing, by a computer storage unit, the data indicativeof the plurality of claims; automatically determining, by a segmentationplatform processor of a claims processing computer system, for eachclaim of the plurality of claims, a segment classification based onapplication of segmentation logic applicable to the claim; receiving, bythe load balancing and assignment engine from the segmentation platformprocessor, a segment classification indication for each of the pluralityof claims comprising one of a first segment classification indicationand a second segment classification indication; accessing, by the loadbalancing and assignment engine, a claim handler database to identifyeligible claim handlers for each of the plurality of claims based on theone of the first segment classification indication or the second segmentclassification indication for each of the plurality of claims;determining, by the load balancing and assignment engine, a load foreach of the eligible claim handlers for each of the plurality of claims;automatically selecting, by the load balancing and assignment engine,for each of the plurality of claims based on the adjusted load for eachof the corresponding eligible claim handlers for the claim, assignmentof each claim to one of the eligible claim handlers and a case manager;and transmitting, by the load balancing and assignment engine, anelectronic file for each of the plurality of claims to the assigned oneof the eligible claim handlers and the case manager.
 10. Thecomputer-implemented method of claim 9, wherein the data indicative of aplurality of claims is received by the communication device of a claimintake computer system.
 11. The computer-implemented method of claim 9,wherein the segmentation logic comprises first segmentation logicapplicable to claims of the first coverage type and second segmentationlogic applicable to claims of the second coverage type.
 12. Thecomputer-implemented method of claim 11, wherein automaticallydetermining the segment classification for each claim of the pluralityof claims comprises: analyzing, for each of the claims associated withthe first coverage type, the received data associated with the claim, inaccordance with the first segmentation logic, to determine the firstsegment classification appropriate for the claim; and analyzing, foreach of the claims associated with the second coverage type, thereceived data associated with the claim, in accordance with the secondsegmentation logic different from the first segmentation logic, todetermine the second segment classification appropriate for the claim.13. The computer-implemented method of claim 12, further comprisingconfiguring the first segmentation logic and the second segmentationlogic with a configurable segment ranking table.
 14. Thecomputer-implemented method of claim 9, wherein determining the load foreach of the eligible claim handlers for each of the plurality of claimsis based upon an adjusted load, the adjusted load based on: dataindicative of a number of other claims currently assigned to each of theeligible claim handlers; and one or more of data indicative of paid timeoff information for each of the eligible claim handlers and a loadfactor indicative of a claim-handling ability of the eligible claimhandlers.
 15. The computer-implemented method of claim 9, wherein stepsperformed by at least one of the segmentation platform processor and theload balancing and assignment engine are implemented by a predictivemodel trained with historical claim information.
 16. Thecomputer-implemented method of claim 15, further comprising training, bya model training component computer processor based upon the historicalclaim information, a predictive model to determine segmentclassifications for claims by: receiving a plurality of historical datasets comprising the historical claim information from the computerstorage unit; applying one or both of a variable association datareduction process and a granularity reduction process to the pluralityof historical data sets to generate reduced historical data sets andreduce a dimensionality of the plurality of historical data sets; andtraining, based upon the reduced historical data sets, the predictivemodel to determine segment classifications based upon claims data.